Josh Dillon, Last Revised January 2022
This notebook examines an individual antenna's performance over a whole season. This notebook parses information from each nightly rtp_summarynotebook (as saved to .csvs) and builds a table describing antenna performance. It also reproduces per-antenna plots from each auto_metrics notebook pertinent to the specific antenna.
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))
# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "004"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder
# Use environment variables to figure out path to the csvs and auto_metrics
antenna = str(int(os.environ["ANTENNA"]))
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')
antenna = "163" csv_folder = "/home/obs/src/H6C_Notebooks/_rtp_summary_" auto_metrics_folder = "/home/obs/src/H6C_Notebooks/auto_metrics_inspect"
display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))
import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna
# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, 'rtp_summary_table*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')
Found 30 csvs in /home/obs/src/H6C_Notebooks/_rtp_summary_ Found 28 auto_metrics notebooks in /home/obs/src/H6C_Notebooks/auto_metrics_inspect
# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0
def jd_to_summary_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'
def jd_to_auto_metrics_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'
this_antenna = None
jds = []
# parse information about antennas and nodes
for csv in csvs:
df = pd.read_csv(csv)
for n in range(len(df)):
# Add this day to the antenna
row = df.loc[n]
if isinstance(row['Ant'], str) and '<a href' in row['Ant']:
antnum = int(row['Ant'].split('</a>')[0].split('>')[-1]) # it's a link, extract antnum
else:
antnum = int(row['Ant'])
if antnum != int(antenna):
continue
if np.issubdtype(type(row['Node']), np.integer):
row['Node'] = str(row['Node'])
if type(row['Node']) == str and row['Node'].isnumeric():
row['Node'] = 'N' + ('0' if len(row['Node']) == 1 else '') + row['Node']
if this_antenna is None:
this_antenna = Antenna(row['Ant'], row['Node'])
jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
jds.append(jd)
this_antenna.add_day(jd, row)
break
# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]
df = pd.DataFrame(to_show)
# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
df[col] = bar_cols[col]
z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
df[col] = z_score_cols[col]
ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
df[col] = ant_metrics_cols[col]
redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]
for col in redcal_cols:
df[col] = redcal_cols[col]
# style dataframe
table = df.style.hide_index()\
.applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
.background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
.background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
.applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
.format({col: '{:,.4f}'.format for col in z_score_cols}) \
.format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
.format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
.set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])
This table reproduces each night's row for this antenna from the RTP Summary notebooks. For more info on the columns, see those notebooks, linked in the JD column.
display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))
| JDs | A Priori Status | Auto Metrics Flags | Dead Fraction in Ant Metrics (Jee) | Dead Fraction in Ant Metrics (Jnn) | Crossed Fraction in Ant Metrics | Flag Fraction Before Redcal | Flagged By Redcal chi^2 Fraction | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | Average Dead Ant Metric (Jee) | Average Dead Ant Metric (Jnn) | Average Crossed Ant Metric | Median chi^2 Per Antenna (Jee) | Median chi^2 Per Antenna (Jnn) |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2459846 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 33.33% | 0.00% | 1.255008 | 0.109983 | 0.419487 | -0.869708 | -0.822403 | 0.485884 | -0.202431 | 1.642427 | 0.8373 | 0.6718 | 0.4913 | 1.680339 | 1.406950 |
| 2459845 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 16.02% | 0.00% | 0.781907 | 0.273222 | 0.692061 | -0.572595 | -1.294675 | -0.329542 | 0.030173 | 0.945489 | 0.7333 | 0.7471 | 0.3845 | 1.223938 | 1.221412 |
| 2459844 | digital_ok | 0.00% | 100.00% | 100.00% | 0.00% | - | - | 0.851178 | 1.491841 | -0.646425 | -0.605713 | -0.487480 | 0.616752 | 0.548918 | 2.548471 | 0.0286 | 0.0264 | 0.0011 | nan | nan |
| 2459842 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | -0.281035 | 0.591579 | 0.576251 | 0.438472 | -0.325734 | 0.995288 | -0.130229 | 0.317509 | 0.7648 | 0.6945 | 0.2470 | 2.202509 | 2.134373 |
| 2459841 | digital_ok | 0.00% | 100.00% | 100.00% | 0.00% | - | - | -0.021521 | 0.374743 | -0.274934 | -0.644139 | -0.949728 | 0.434897 | 0.606269 | 1.368514 | 0.0283 | 0.0269 | 0.0013 | nan | nan |
| 2459840 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 335.981025 | 169.197096 | 121.036866 | 54.694165 | 1639.914237 | 305.932091 | 3819.395527 | 852.414096 | 0.0207 | 0.0172 | 0.0021 | nan | nan |
| 2459839 | digital_ok | 100.00% | - | - | - | - | - | nan | nan | inf | inf | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 2459838 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | -0.293841 | -0.811562 | -0.265738 | -0.675083 | -0.929714 | -0.584628 | -0.540672 | 0.920113 | 0.7642 | 0.7299 | 0.3893 | 2.418838 | 2.142951 |
| 2459836 | digital_ok | - | 100.00% | 100.00% | 0.00% | - | - | nan | nan | nan | nan | nan | nan | nan | nan | 0.0394 | 0.0706 | 0.0094 | nan | nan |
| 2459835 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | -1.284801 | -0.557182 | -0.117510 | 0.787945 | 0.822232 | 0.393600 | 5.678911 | 4.989273 | 0.0388 | 0.0709 | 0.0093 | nan | nan |
| 2459833 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | -0.094689 | -0.522170 | 0.114123 | -0.115170 | 3.700766 | 3.507707 | 6.465173 | 8.586355 | 0.0331 | 0.0379 | 0.0046 | nan | nan |
| 2459832 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 0.523124 | -0.688895 | -0.551055 | -0.868038 | -1.557011 | 0.632228 | -0.334033 | 2.154181 | 0.8129 | 0.5636 | 0.5675 | 1.813805 | 1.737863 |
| 2459831 | digital_ok | 0.00% | 100.00% | 100.00% | 0.00% | - | - | 0.337680 | 0.286988 | -0.896102 | -0.873271 | -0.004178 | -0.233583 | 0.420249 | 1.461535 | 0.0267 | 0.0256 | 0.0019 | nan | nan |
| 2459830 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 2.63% | 0.00% | 0.114572 | 0.010609 | -0.277978 | -0.569982 | -1.247299 | -0.269946 | 0.318285 | 1.189087 | 0.8105 | 0.5750 | 0.5536 | 1.736995 | 1.275000 |
| 2459829 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 0.591502 | -0.483318 | 0.374213 | -0.833291 | -0.778491 | 0.628998 | -0.292609 | 2.866453 | 0.7653 | 0.6927 | 0.4055 | 0.999127 | 0.865650 |
| 2459828 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.637727 | -0.401309 | 0.164774 | -0.822043 | -0.636341 | -0.125017 | 0.295332 | 1.259164 | 0.8075 | 0.5831 | 0.5343 | 0.000000 | 0.000000 |
| 2459827 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 0.586790 | -0.025917 | 0.022723 | -0.846513 | -1.204998 | -0.092183 | 0.672837 | 2.061765 | 0.7749 | 0.7009 | 0.4030 | 0.000000 | 0.000000 |
| 2459826 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 84.21% | 0.00% | 0.568436 | 0.044568 | -0.131778 | -0.771030 | -1.227215 | -0.315620 | -0.274563 | 1.682189 | 0.8084 | 0.6076 | 0.4986 | 0.000000 | 0.000000 |
| 2459825 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 0.343298 | -0.307203 | 0.020743 | -0.395088 | -0.680691 | -0.564057 | 0.289178 | 1.276085 | 0.8080 | 0.6202 | 0.4989 | 1.565560 | 1.155008 |
| 2459824 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 0.596406 | -0.473334 | 0.152126 | -0.531261 | 0.826571 | 2.250334 | 4.494332 | 6.968808 | 0.7451 | 0.7568 | 0.3542 | 5.684988 | 6.577228 |
| 2459823 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 0.504561 | -0.105032 | 0.014762 | -0.412099 | -0.282217 | 1.130897 | -0.089241 | 1.512185 | 0.7796 | 0.6698 | 0.4482 | 2.647421 | 2.889904 |
| 2459822 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 0.363027 | -0.119315 | 0.168969 | -0.899812 | -0.654998 | -0.151487 | 0.640295 | -0.023948 | 0.8156 | 0.6462 | 0.4920 | 2.147480 | 1.705858 |
| 2459821 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 0.330260 | -0.469068 | 0.009171 | -0.968885 | 0.636495 | 1.059377 | -0.168030 | 1.948510 | 0.8018 | 0.6447 | 0.4952 | 2.030322 | 1.853633 |
| 2459820 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 0.53% | 0.476826 | 0.155311 | -0.020548 | -0.925102 | -0.504354 | 1.006042 | -0.721900 | 0.553495 | 0.7864 | 0.6999 | 0.4072 | 2.197759 | 1.964650 |
| 2459817 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 0.378260 | -0.550673 | 0.005525 | -0.876032 | 0.000503 | -0.766229 | 0.711787 | 0.716658 | 0.8089 | 0.6749 | 0.4892 | 2.192128 | 2.086517 |
| 2459816 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 0.791655 | -0.022235 | 0.015823 | -0.230319 | -0.026581 | 1.769969 | -0.228758 | 3.151570 | 0.8476 | 0.6184 | 0.5720 | 2.005463 | 1.658412 |
| 2459815 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 0.253947 | -0.306894 | -0.028162 | -0.293744 | -0.494246 | -0.715435 | -0.085276 | 2.316710 | 0.8001 | 0.6802 | 0.5042 | 2.224198 | 2.052190 |
| 2459814 | digital_ok | 0.00% | - | - | - | - | - | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 2459813 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 0.53% | 0.712882 | 1.168031 | 0.009962 | -0.929585 | 0.463851 | 0.218777 | -0.440206 | 1.787300 | 0.7971 | 0.7092 | 0.4136 | 2.396780 | 1.978120 |
auto_metrics notebooks.¶htmls_to_display = []
for am_html in auto_metric_htmls:
html_to_display = ''
# read html into a list of lines
with open(am_html) as f:
lines = f.readlines()
# find section with this antenna's metric plots and add to html_to_display
jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
try:
section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
except ValueError:
continue
html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
for line in lines[section_start_line + 1:]:
html_to_display += line
if '<hr' in line:
htmls_to_display.append(html_to_display)
break
These figures are reproduced from auto_metrics notebooks. For more info on the specific plots and metrics, see those notebooks (linked at the JD). The most recent 100 days (at most) are shown.
for i, html_to_display in enumerate(htmls_to_display):
if i == 100:
break
display(HTML(html_to_display))
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 163 | N14 | digital_ok | nn Temporal Discontinuties | 1.642427 | 1.255008 | 0.109983 | 0.419487 | -0.869708 | -0.822403 | 0.485884 | -0.202431 | 1.642427 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 163 | N14 | digital_ok | nn Temporal Discontinuties | 0.945489 | 0.273222 | 0.781907 | -0.572595 | 0.692061 | -0.329542 | -1.294675 | 0.945489 | 0.030173 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 163 | N14 | digital_ok | nn Temporal Discontinuties | 2.548471 | 0.851178 | 1.491841 | -0.646425 | -0.605713 | -0.487480 | 0.616752 | 0.548918 | 2.548471 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 163 | N14 | digital_ok | nn Temporal Variability | 0.995288 | -0.281035 | 0.591579 | 0.576251 | 0.438472 | -0.325734 | 0.995288 | -0.130229 | 0.317509 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 163 | N14 | digital_ok | nn Temporal Discontinuties | 1.368514 | -0.021521 | 0.374743 | -0.274934 | -0.644139 | -0.949728 | 0.434897 | 0.606269 | 1.368514 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 163 | N14 | digital_ok | ee Temporal Discontinuties | 3819.395527 | 335.981025 | 169.197096 | 121.036866 | 54.694165 | 1639.914237 | 305.932091 | 3819.395527 | 852.414096 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 163 | N14 | digital_ok | nn Shape | nan | nan | nan | inf | inf | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 163 | N14 | digital_ok | nn Temporal Discontinuties | 0.920113 | -0.811562 | -0.293841 | -0.675083 | -0.265738 | -0.584628 | -0.929714 | 0.920113 | -0.540672 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 163 | N14 | digital_ok | ee Temporal Discontinuties | 5.678911 | -0.557182 | -1.284801 | 0.787945 | -0.117510 | 0.393600 | 0.822232 | 4.989273 | 5.678911 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 163 | N14 | digital_ok | nn Temporal Discontinuties | 8.586355 | -0.522170 | -0.094689 | -0.115170 | 0.114123 | 3.507707 | 3.700766 | 8.586355 | 6.465173 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 163 | N14 | digital_ok | nn Temporal Discontinuties | 2.154181 | 0.523124 | -0.688895 | -0.551055 | -0.868038 | -1.557011 | 0.632228 | -0.334033 | 2.154181 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 163 | N14 | digital_ok | nn Temporal Discontinuties | 1.461535 | 0.337680 | 0.286988 | -0.896102 | -0.873271 | -0.004178 | -0.233583 | 0.420249 | 1.461535 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 163 | N14 | digital_ok | nn Temporal Discontinuties | 1.189087 | 0.114572 | 0.010609 | -0.277978 | -0.569982 | -1.247299 | -0.269946 | 0.318285 | 1.189087 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 163 | N14 | digital_ok | nn Temporal Discontinuties | 2.866453 | -0.483318 | 0.591502 | -0.833291 | 0.374213 | 0.628998 | -0.778491 | 2.866453 | -0.292609 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 163 | N14 | digital_ok | nn Temporal Discontinuties | 1.259164 | -0.401309 | 0.637727 | -0.822043 | 0.164774 | -0.125017 | -0.636341 | 1.259164 | 0.295332 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 163 | N14 | digital_ok | nn Temporal Discontinuties | 2.061765 | 0.586790 | -0.025917 | 0.022723 | -0.846513 | -1.204998 | -0.092183 | 0.672837 | 2.061765 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 163 | N14 | digital_ok | nn Temporal Discontinuties | 1.682189 | 0.044568 | 0.568436 | -0.771030 | -0.131778 | -0.315620 | -1.227215 | 1.682189 | -0.274563 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 163 | N14 | digital_ok | nn Temporal Discontinuties | 1.276085 | -0.307203 | 0.343298 | -0.395088 | 0.020743 | -0.564057 | -0.680691 | 1.276085 | 0.289178 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 163 | N14 | digital_ok | nn Temporal Discontinuties | 6.968808 | 0.596406 | -0.473334 | 0.152126 | -0.531261 | 0.826571 | 2.250334 | 4.494332 | 6.968808 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 163 | N14 | digital_ok | nn Temporal Discontinuties | 1.512185 | -0.105032 | 0.504561 | -0.412099 | 0.014762 | 1.130897 | -0.282217 | 1.512185 | -0.089241 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 163 | N14 | digital_ok | ee Temporal Discontinuties | 0.640295 | 0.363027 | -0.119315 | 0.168969 | -0.899812 | -0.654998 | -0.151487 | 0.640295 | -0.023948 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 163 | N14 | digital_ok | nn Temporal Discontinuties | 1.948510 | -0.469068 | 0.330260 | -0.968885 | 0.009171 | 1.059377 | 0.636495 | 1.948510 | -0.168030 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 163 | N14 | digital_ok | nn Temporal Variability | 1.006042 | 0.476826 | 0.155311 | -0.020548 | -0.925102 | -0.504354 | 1.006042 | -0.721900 | 0.553495 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 163 | N14 | digital_ok | nn Temporal Discontinuties | 0.716658 | 0.378260 | -0.550673 | 0.005525 | -0.876032 | 0.000503 | -0.766229 | 0.711787 | 0.716658 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 163 | N14 | digital_ok | nn Temporal Discontinuties | 3.151570 | -0.022235 | 0.791655 | -0.230319 | 0.015823 | 1.769969 | -0.026581 | 3.151570 | -0.228758 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 163 | N14 | digital_ok | nn Temporal Discontinuties | 2.316710 | -0.306894 | 0.253947 | -0.293744 | -0.028162 | -0.715435 | -0.494246 | 2.316710 | -0.085276 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 163 | N14 | digital_ok | nn Shape | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 163 | N14 | digital_ok | nn Temporal Discontinuties | 1.787300 | 1.168031 | 0.712882 | -0.929585 | 0.009962 | 0.218777 | 0.463851 | 1.787300 | -0.440206 |